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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

期数:

2025年07期

页码:

156-164

栏目:

新型计算应用系统

出版日期:

2025-07-10

文章信息/Info

Title:

Parking Allocation Method Based on Adaptive Mutation Strategy of Population Density

文章编号:

1673-629X(2025)07-0156-09

作者:

蹇文康1; 2; 李涛1; 2; 吕林3; 项鹏4; 何柳5

1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065;

2. 武汉科技大学 智能信息处理与实时工业系统湖北省重点实验室,湖北 武汉 430065;

3. 湖北科技职业学院 电信工程学院,湖北 武汉 430074;

4. 武汉博睿英特科技有限公司,湖北 武汉 430070;

5. 武汉智园智慧电梯科技有限公司,湖北 武汉 430076

Author(s):

JIAN Wen-kang1; 2; LI Tao1; 2; LYU Lin3; XIANG Peng4; HE Liu5

1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;

2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan University of Science and Technology,Wuhan 430065,China;

3. School of Telecommunication Engineering,Hubei Vocational College of Science and Technology,Wuhan 430074,China;

4. Wuhan Borui Yingte Technology Co. ,Ltd. ,Wuhan 430070,China;

5. Wuhan Zhiyuan Intelligent Elevator Technology Co. ,Ltd. ,Wuhan 430076,China

关键词:

停机位分配; 多目标优化; 遗传算法; 种群密度; 自适应变异

Keywords:

parking allocation; multi-objective optimization; genetic algorithm; population density; adaptive mutation

分类号:

TP391

DOI:

10.20165/j.cnki.ISSN1673-629X.2025.0046

摘要:

停机位分配问题需要将有限的资源(即机场的停机位)合理分配给需要这些资源的任务(即到达和离港的航班),属于高维离散多目标优化问题。 现阶段研究方法主要是建立数学模型,并使用各种智能算法来求解获得最优解集。 然而,针对停机位问题中高维离散化的特征,多数算法在进化过程中,使用非支配排序和精英策略来筛选后代,可能导致种群的多样性变差、帕累托前沿的不均匀分布等问题,从而降低解集的质量。 该文提出了一种基于种群密度自适应变异策略的算法,通过智能地调节变异操作的方向和方位,有效维护并增强目标搜索空间内的探索广度与深度,用于保持目标空间和种群的多样性。 将该算法与多种具有代表性的遗传算法和智能算法进行比较。 实验结果表明,在有限时间内该算法在决策空间中能找到更多有效的解,并且能更好地保持决策空间和目标空间多样性和收敛性的平衡,有效指导停机位分配,整体效果要好于现有遗传算法和智能算法。

Abstract:

The parking allocation problem requires the reasonable allocation of limited resources (the parking space at the airport) to tasks that require these resources (Arriving and departing flights). It is a high-dimensional discrete multi-objective optimization problem. At present,the main research methods are to establish mathematical models and use various intelligent algorithms to obtain the optimal solution set. However, for the characteristics of the high dimensional discretization in the gate problem, most algorithms use non - dominated sorting and elite strategy to select offspring during the evolution process,which may lead to poor diversity of the population and uneven distribution of the Pareto frontier,thus reducing the quality of solution set. We propose an algorithm based on a population density-based adaptive mutation strategy. By intelligently adjusting the direction and orientation of the mutation operation,it effectively maintains and enhances the exploration breadth and depth within the target search space,thereby preserving the diversity of the target space and the population. We compare the proposed algorithm with the representative genetic algorithms. Experimental results show that the proposed algorithm can find more effective solutions in the decision space,and can better maintain the balance between the diversity and convergence of the decision space and the target space,effectively guide the allocation of parking positions,and the overall effect is better than that of the existing genetic algorithm and intelligent algorithm.

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更新日期/Last Update: 2025-07-10